03 / Intelligence

    AI & Automation.

    AI that earns its place in your product.

    AI & Automation

    We build AI features that actually move metrics — copilots, recommendation systems, autonomous workflows — engineered with the same rigour as the rest of your product.

    The Problem

    Most AI in products is a demo, not a feature.

    01

    "Powered by AI" labels with no measurable impact

    02

    Hallucinations that erode user trust within weeks

    03

    No evals, no guardrails, no observability

    04

    Prompt spaghetti instead of real engineering

    Our Approach

    AI as engineered systems, not magic spells.

    Every AI feature we ship has evals, guardrails, fallback paths, and clear KPIs. We treat models like any other dependency — version-controlled, observable, replaceable.

    01

    Evals first

    No feature ships without a measurable quality bar.

    02

    Guardrails

    Input validation, output filtering, safety classifiers built in.

    03

    Hybrid retrieval

    RAG, structured tools, and fine-tuning combined where each wins.

    04

    Observability

    Every prompt, response, and decision logged and queryable.

    What You Get

    Deliverables.

    • AI feature integrated into your product
    • Evaluation suite & quality dashboard
    • Prompt + tool architecture
    • Vector store + retrieval pipeline
    • Safety guardrails
    • Cost & latency monitoring
    • Runbook for ongoing tuning

    Built With

    Tech stack.

    Models

    GPT-5ClaudeGeminiOpen source

    Orchestration

    LangGraphVercel AI SDKCustom agents

    Retrieval

    pgvectorPineconeHybrid search

    Ops

    LangfuseBraintrustCustom evals

    Timeline

    From kickoff to live.

    01

    Use-case design

    Week 1

    Identify highest-leverage AI surfaces with clear KPIs.

    02

    Prototype

    Week 2–3

    Working spike against a real eval set.

    03

    Productionise

    Week 3–6

    Guardrails, observability, integration.

    04

    Tune

    Week 6–8

    A/B against baseline, iterate on prompts + retrieval.

    05

    Handover

    Week 8+

    Eval suite, runbook, knowledge transfer.

    Selected client launches

    Real businesses. Real work.

    All client stories

    Questions

    FAQs.

    Which model do you use?

    +

    Whichever wins on your eval set. We benchmark across providers and pick per-feature.

    How do you handle hallucinations?

    +

    Through grounded retrieval, structured outputs, validators, and human-in-the-loop where the cost of wrong is high.

    Will costs blow up?

    +

    We design with cost budgets per request and monitor spend in real time. Caching + routing keep costs predictable.

    Pairs Well With

    Related capabilities.

    Let's build

    Ready to ship ai & automation?

    Start a project